{"id":13415830,"url":"https://github.com/nashory/gans-awesome-applications","last_synced_at":"2025-05-14T15:06:37.368Z","repository":{"id":40602917,"uuid":"106639043","full_name":"nashory/gans-awesome-applications","owner":"nashory","description":"Curated list of awesome GAN applications and demo","archived":false,"fork":false,"pushed_at":"2023-08-24T07:51:31.000Z","size":239,"stargazers_count":5064,"open_issues_count":18,"forks_count":814,"subscribers_count":206,"default_branch":"master","last_synced_at":"2025-05-07T20:01:59.890Z","etag":null,"topics":["applications","curated-list","demonstration","generative-adversarial-network","github","papers"],"latest_commit_sha":null,"homepage":null,"language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/nashory.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2017-10-12T03:19:02.000Z","updated_at":"2025-05-06T14:56:25.000Z","dependencies_parsed_at":"2024-01-13T06:21:24.330Z","dependency_job_id":"1c838ab8-97de-476d-b315-c70aee546a73","html_url":"https://github.com/nashory/gans-awesome-applications","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nashory%2Fgans-awesome-applications","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nashory%2Fgans-awesome-applications/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nashory%2Fgans-awesome-applications/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nashory%2Fgans-awesome-applications/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/nashory","download_url":"https://codeload.github.com/nashory/gans-awesome-applications/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":254079042,"owners_count":22011085,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["applications","curated-list","demonstration","generative-adversarial-network","github","papers"],"created_at":"2024-07-30T21:00:52.408Z","updated_at":"2025-05-14T15:06:37.315Z","avatar_url":"https://github.com/nashory.png","language":null,"funding_links":[],"categories":["Others","Uncategorized","Awesome Lists","CV","Table of Contents","Machine Learning","Other Lists","Awesome List","GAN"],"sub_categories":["Uncategorized","JavaScript","TeX Lists","Ukraine"],"readme":"![banner](https://github.com/nashory/gans-awesome-applications/blob/master/jpg/gans.jpg)\n\n# gans-awesome-applications\nCurated list of awesome GAN applications and demonstrations.  \n\n__Note: General GAN papers targeting simple image generation such as DCGAN, BEGAN etc. are not included in the list. I mainly care about applications.__\n\n## The landmark papers that I respect.\n+ Generative Adversarial Networks, [[paper]](https://arxiv.org/abs/1406.2661), [[github]](https://github.com/goodfeli/adversarial)\n+ Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, [[paper]](https://arxiv.org/pdf/1511.06434), [[github]](https://github.com/soumith/dcgan.torch)\n+ Improved Techniques for Training GANs, [[paper]](https://arxiv.org/pdf/1606.03498.pdf), [[github]](https://github.com/openai/improved-gan)\n+ BEGAN: Boundary Equilibrium Generative Adversarial Networks, [[paper]](https://arxiv.org/pdf/1703.10717), [[github]](https://github.com/carpedm20/BEGAN-tensorflow)\n\n-----\n\n## Contents\n__Use this contents list or simply press \u003ckbd\u003ecommand\u003c/kbd\u003e + \u003ckbd\u003eF\u003c/kbd\u003e to search for a keyword__\n+ [Applications using GANs](#applications-using-gans)\n    + [Font generation](#font-generation)\n    + [Anime character generation](#anime-character-generation)\n    + [Interactive Image generation](#interactive-image-generation)\n    + [Text2Image (text to image)](#text2image-text-to-image)\n    + [3D Object generation](#3d-object-generation)\n    + [Image Editing](#image-editing)\n    + [Face Aging](#face-aging)\n    + [Human Pose Estimation](#human-pose-estimation)\n    + [Domain-transfer (e.g. style-transfer, pix2pix, sketch2image)](#domain-transfer-eg-style-transfer-pix2pix-sketch2image)\n    + [Image Inpainting (hole filling)](#image-inpainting-hole-filling)\n    + [Super-resolution](#super-resolution)\n    + [High-resolution image generation (large-scale image)](#high-resolution-image-generation-large-scale-image)\n    + [Adversarial Examples (Defense vs Attack)](#adversarial-examples-defense-vs-attack)\n    + [Visual Saliency Prediction (attention prediction)](#visual-saliency-prediction-attention-prediction)\n    + [Object Detection/Recognition](#object-detectionrecognition)\n    + [Robotics](#robotics)\n    + [Video (generation/prediction)](#video-generationprediction)\n    + [Synthetic Data Generation](#synthetic-data-generation)\n    + [Others](#others)\n+ [Did not use GAN, but still interesting applications](#did-not-use-gan-but-still-interesting-applications)\n    + [Real-time face reconstruction](#real-time-face-reconstruction)\n    + [Super-resolution](#super-resolution-1)\n    + [Photorealistic Image generation (e.g. pix2pix, sketch2image)](#photorealistic-image-generation-eg-pix2pix-sketch2image)\n    + [Human Pose Estimation](#human-pose-estimation-1)\n    + [3D Object generation](#3d-object-generation-1)\n+ [GAN tutorials with easy and simple example code for starters](#gan-tutorials-with-easy-and-simple-example-code-for-starters)\n+ [Implementations of various types of GANs collection](#implementations-of-various-types-of-gans-collection)\n+ [Trendy AI-application Articles](#trendy-ai-application-articles)\n\n-----\n\n## Applications using GANs\n\n### Font generation\n+ Learning Chinese Character style with conditional GAN, [[blog]](https://kaonashi-tyc.github.io/2017/04/06/zi2zi.html), [[github]](https://github.com/kaonashi-tyc/zi2zi)\n+ Artistic Glyph Image Synthesis via One-Stage Few-Shot Learning, [[paper]](http://arxiv.org/abs/1910.04987), [[github]](https://github.com/hologerry/AGIS-Net)\n+ Attribute2Font: Creating Fonts You Want From Attributes, [[paper]](https://arxiv.org/abs/2005.07865), [[github]](https://github.com/hologerry/Attr2Font)\n\n### Anime character generation\n+ Towards the Automatic Anime Characters Creation with Generative Adversarial Networks, [[paper]](https://arxiv.org/pdf/1708.05509)\n+ [Project] A simple PyTorch Implementation of Generative Adversarial Networks, focusing on anime face drawing, [[github]](https://github.com/jayleicn/animeGAN)\n+ [Project] A simple, clean TensorFlow implementation of Generative Adversarial Networks with a focus on modeling illustrations, [[github]](https://github.com/tdrussell/IllustrationGAN)\n+ [Project] Keras-GAN-Animeface-Character, [[github]](https://github.com/forcecore/Keras-GAN-Animeface-Character)\n+ [Project] A DCGAN to generate anime faces using custom mined dataset, [[github]](https://github.com/pavitrakumar78/Anime-Face-GAN-Keras)\n\n### Interactive Image generation\n+ Generative Visual Manipulation on the Natural Image Manifold, [[paper]](https://arxiv.org/pdf/1609.03552), [[github]](https://github.com/junyanz/iGAN)\n+ Neural Photo Editing with Introspective Adversarial Networks, [[paper]](http://arxiv.org/abs/1609.07093), [[github]](https://github.com/ajbrock/Neural-Photo-Editor)\n\n### Text2Image (text to image)\n+ TAC-GAN – Text Conditioned Auxiliary Classifier Generative Adversarial Network, [[paper]](https://arxiv.org/pdf/1703.06412.pdf), [[github]](https://github.com/dashayushman/TAC-GAN)\n+ StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks, [[paper]](https://arxiv.org/pdf/1612.03242.pdf), [[github]](https://github.com/hanzhanggit/StackGAN)\n+ Generative Adversarial Text to Image Synthesis, [[paper]](https://arxiv.org/pdf/1605.05396.pdf), [[github]](https://github.com/paarthneekhara/text-to-image), [[github]](https://github.com/reedscot/icml2016)\n+ Learning What and Where to Draw, [[paper]](http://www.scottreed.info/files/nips2016.pdf), [[github]](https://github.com/reedscot/nips2016)\n\n### 3D Object generation\n+ Parametric 3D Exploration with Stacked Adversarial Networks, [[github]](https://github.com/maxorange/pix2vox), [[youtube]](https://www.youtube.com/watch?v=ITATOXVvWEM)\n+ Learning a Probabilistic Latent Space of Object\nShapes via 3D Generative-Adversarial Modeling, [[paper]](http://papers.nips.cc/paper/6096-learning-a-probabilistic-latent-space-of-object-shapes-via-3d-generative-adversarial-modeling.pdf), [[github]](https://github.com/zck119/3dgan-release), [[youtube]](https://www.youtube.com/watch?v=HO1LYJb818Q)\n+ 3D Shape Induction from 2D Views of Multiple Objects, [[paper]](https://arxiv.org/pdf/1612.05872.pdf)\n+ Fully Convolutional Refined Auto-Encoding Generative Adversarial Networks for 3D Multi Object Scenes, [[github]](https://github.com/yunishi3/3D-FCR-alphaGAN), [[blog]](https://becominghuman.ai/3d-multi-object-gan-7b7cee4abf80)\n\n### Image Editing\n+ Invertible Conditional GANs for image editing, [[paper]](https://arxiv.org/abs/1611.06355), [[github]](https://github.com/Guim3/IcGAN)\n+ Image De-raining Using a Conditional Generative Adversarial Network, [[paper]](https://arxiv.org/abs/1701.05957), [[github]](https://github.com/hezhangsprinter/ID-CGAN)\n\n### Face Aging\n+ Age Progression/Regression by Conditional Adversarial Autoencoder, [[paper]](https://arxiv.org/pdf/1702.08423), [[github]](https://github.com/ZZUTK/Face-Aging-CAAE)\n+ CAN: Creative Adversarial Networks Generating “Art” by Learning About Styles and Deviating from Style Norms, [[paper]](https://arxiv.org/pdf/1706.07068.pdf)\n+ FACE AGING WITH CONDITIONAL GENERATIVE ADVERSARIAL NETWORKS, [[paper]](https://arxiv.org/pdf/1702.01983.pdf)\n\n### Human Pose Estimation\n+ Joint Discriminative and Generative Learning for Person Re-identification, [[paper]](https://arxiv.org/abs/1904.07223), [[github]](https://github.com/NVlabs/DG-Net), [[video]](https://www.youtube.com/watch?v=ubCrEAIpQs4)\n+ Pose Guided Person Image Generation, [[paper]](https://arxiv.org/abs/1705.09368)\n\n### Domain-transfer (e.g. style-transfer, pix2pix, sketch2image)\n+ Image-to-Image Translation with Conditional Adversarial Networks, [[paper]](https://arxiv.org/pdf/1611.07004), [[github]](https://github.com/phillipi/pix2pix), [[youtube]](https://www.youtube.com/watch?v=VVqxbmUJorQ)\n+ Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks, [[paper]](https://arxiv.org/pdf/1703.10593.pdf), [[github]](https://github.com/junyanz/CycleGAN), [[youtube]](https://www.youtube.com/watch?v=JzgOfISLNjk)\n+ Learning to Discover Cross-Domain Relations with Generative Adversarial Networks, [[paper]](https://arxiv.org/pdf/1703.05192.pdf), [[github]](https://github.com/carpedm20/DiscoGAN-pytorch)\n+ Unsupervised Creation of Parameterized Avatars, [[paper]](https://arxiv.org/pdf/1704.05693.pdf)\n+ UNSUPERVISED CROSS-DOMAIN IMAGE GENERATION, [[paper]](https://openreview.net/pdf?id=Sk2Im59ex)\n+ Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks, [[paper]](http://arxiv.org/abs/1604.04382), [[github]](https://github.com/chuanli11/MGANs)\n+ Pixel-Level Domain Transfer  [[paper]](https://arxiv.org/pdf/1603.07442), [[github]](https://github.com/fxia22/PixelDTGAN)\n+ TextureGAN: Controlling Deep Image Synthesis with Texture Patches, [[paper]](https://arxiv.org/pdf/1706.02823.pdf), [[demo]](https://github.com/varunagrawal/t-gan-demo)\n+ Vincent AI Sketch Demo Draws In Throngs at GTC Europe, [[blog](https://blogs.nvidia.com/blog/2017/10/11/vincent-ai-sketch-demo-draws-in-throngs-at-gtc-europe/)], [[youtube]](https://www.youtube.com/watch?v=kIcqXTUMwps)\n+ Deep Photo Style Transfer, [[paper]](https://arxiv.org/pdf/1703.07511.pdf), [[github]](https://github.com/luanfujun/deep-photo-styletransfer)\n\n\n### Image Inpainting (hole filling)\n+ Context Encoders: Feature Learning by Inpainting, [[paper]](https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Pathak_Context_Encoders_Feature_CVPR_2016_paper.pdf), [[github]](https://github.com/pathak22/context-encoder)\n+ Semantic Image Inpainting with Perceptual and Contextual Losses, [[paper]](https://arxiv.org/abs/1607.07539), [[github]](https://github.com/bamos/dcgan-completion.tensorflow)\n+ SEMI-SUPERVISED LEARNING WITH CONTEXT-CONDITIONAL GENERATIVE ADVERSARIAL NETWORKS, [[paper]](https://arxiv.org/pdf/1611.06430v1.pdf)\n+ Generative Face Completion, [[paper]](https://drive.google.com/file/d/0B8_MZ8a8aoSeenVrYkpCdnFRVms/edit), [[github]](https://github.com/Yijunmaverick/GenerativeFaceCompletion)\n\n### Super-resolution\n+ Image super-resolution through deep learning, [[github]](https://github.com/david-gpu/srez)\n+ Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network, [[paper]](https://arxiv.org/abs/1609.04802), [[github]](https://github.com/leehomyc/Photo-Realistic-Super-Resoluton)\n+ High-Quality Face Image Super-Resolution Using Conditional Generative Adversarial Networks, [[paper]](https://arxiv.org/pdf/1707.00737.pdf)\n+ Analyzing Perception-Distortion Tradeoff using Enhanced Perceptual Super-resolution Network, [[paper]](https://arxiv.org/pdf/1811.00344.pdf), [[github]](https://github.com/subeeshvasu/2018_subeesh_epsr_eccvw)\n\n### Image Blending\n+ GP-GAN: Towards Realistic High-Resolution Image Blending, [[paper]](https://arxiv.org/abs/1703.07195), [[github]](https://github.com/wuhuikai/GP-GAN)\n\n### High-resolution image generation (large-scale image)\n+ Generating Large Images from Latent Vectors, [[blog]](http://blog.otoro.net/2016/04/01/generating-large-images-from-latent-vectors/), [[github]](https://github.com/hardmaru/cppn-gan-vae-tensorflow)\n+ PROGRESSIVE GROWING OF GANS FOR IMPROVED QUALITY, STABILITY, AND VARIATION, [[paper]](http://research.nvidia.com/sites/default/files/pubs/2017-10_Progressive-Growing-of//karras2017gan-paper.pdf), [[github]](https://github.com/tkarras/progressive_growing_of_gans)\n\n### Adversarial Examples (Defense vs Attack) \n+ SafetyNet: Detecting and Rejecting Adversarial Examples Robustly, [[paper]](https://arxiv.org/abs/1704.00103)\n+ ADVERSARIAL EXAMPLES FOR GENERATIVE MODELS, [[paper]](https://arxiv.org/pdf/1702.06832.pdf)\n+ Adversarial Examples Generation and Defense Based on Generative Adversarial Network, [[paper]](http://cs229.stanford.edu/proj2016/report/LiuXia-AdversarialExamplesGenerationAndDefenseBasedOnGenerativeAdversarialNetwork-report.pdf)\n\n\n### Visual Saliency Prediction (attention prediction)\n+ SalGAN: Visual Saliency Prediction with Generative Adversarial Networks, [[paper]](https://arxiv.org/pdf/1701.01081), [[github]](https://github.com/imatge-upc/saliency-salgan-2017)\n\n### Object Detection/Recognition\n+ Perceptual Generative Adversarial Networks for Small Object Detection, [[paper]](https://arxiv.org/pdf/1706.05274)\n+ Adversarial Generation of Training Examples for Vehicle License Plate Recognition, [[paper]](https://arxiv.org/pdf/1707.03124.pdf)\n\n### Robotics\n+ Unsupervised Pixel–Level Domain Adaptation with Generative Adversarial Networks, [[paper]](https://arxiv.org/pdf/1612.05424.pdf), [[github]](https://github.com/rhythm92/Unsupervised-Pixel-Level-Domain-Adaptation-with-GAN)\n\n### Video (generation/prediction)\n+ DEEP MULTI-SCALE VIDEO PREDICTION BEYOND MEAN SQUARE ERROR, [[paper]](https://arxiv.org/pdf/1511.05440.pdf), [[github]](https://github.com/dyelax/Adversarial_Video_Generation)\n\n### Synthetic Data Generation\n+ Learning from Simulated and Unsupervised Images through Adversarial Training, [[paper]](https://arxiv.org/pdf/1612.07828.pdf), [[github]](https://github.com/carpedm20/simulated-unsupervised-tensorflow)\n\n### Others\n+ (Physics) Learning Particle Physics by Example:\nLocation-Aware Generative Adversarial Networks for\nPhysics Synthesis, [[paper]](https://arxiv.org/pdf/1701.05927.pdf), [[github]](https://github.com/hep-lbdl/adversarial-jets)\n+ (Games) STYLE TRANSFER GENERATIVE ADVERSARIAL NETWORKS:\nLEARNING TO PLAY CHESS DIFFERENTLY, [[paper]](https://openreview.net/pdf?id=HkpbnufYe), [[github]](https://github.com/2014mchidamb/AdversarialChess)\n+ (General) Spectral Normalization for Generative Adversarial Networks, [[paper]](https://openreview.net/pdf?id=B1QRgziT-), [[github]](https://github.com/minhnhat93/tf-SNDCGAN)\n-----\n\n## Did not use GAN, but still interesting applications.\n\n### Real-time face reconstruction\n+ Model-based Deep Convolutional Face Autoencoder for Unsupervised Monocular Reconstruction, [[paper]](https://arxiv.org/pdf/1703.10580.pdf), [[github]](https://github.com/waxz/MoFA), [[youtube]](https://www.youtube.com/watch?v=uIMpHZYB8fI)\n\n### Super-resolution\n+ Learning to Simplify:\nFully Convolutional Networks for Rough Sketch Cleanup, [[paper]](http://delivery.acm.org/10.1145/2930000/2925972/a121-simo-serra.pdf?ip=111.91.137.238\u0026id=2925972\u0026acc=ACTIVE%20SERVICE\u0026key=58C7DD92F91E3631%2E58C7DD92F91E3631%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35\u0026CFID=818332500\u0026CFTOKEN=94661101\u0026__acm__=1507786813_0e5b28dfb97e654d0126d61b0aa592f4), [[site link]](http://hi.cs.waseda.ac.jp/~esimo/en/research/sketch/), [[youtube]](https://www.youtube.com/watch?v=4MfG9CDufPA)\n\n### Photorealistic Image generation (e.g. pix2pix, sketch2image)\n+ The Sketchy Database: Learning to Retrieve Badly Drawn Bunnies, [[paper]](http://delivery.acm.org/10.1145/2930000/2925954/a119-sangkloy.pdf?ip=111.91.137.238\u0026id=2925954\u0026acc=CHORUS\u0026key=58C7DD92F91E3631%2E58C7DD92F91E3631%2E4D4702B0C3E38B35%2E6D218144511F3437\u0026CFID=818332500\u0026CFTOKEN=94661101\u0026__acm__=1507787415_cb950c300370fc27da68920a0d5b5178), [[youtube]](https://www.youtube.com/watch?v=a3sgFQjEfp4)\n+ PatchMatch: A Randomized Correspondence Algorithm for Structural Image Editing, [[paper]](https://www.researchgate.net/profile/Eli_Shechtman/publication/220184392_PatchMatch_A_Randomized_Correspondence_Algorithm_for_Structural_Image_Editing/links/02e7e520897b12bf0f000000.pdf), [[github]](https://github.com/younesse-cv/PatchMatch), [[youtube]](https://www.youtube.com/watch?v=n3aoc36V8LM)\n\n### Human Pose Estimation\n+ Knowledge-Guided Deep Fractal Neural Networks for Human Pose Estimation, [[paper]](https://arxiv.org/pdf/1705.02407.pdf), [[github]](https://github.com/Guanghan/GNet-pose)\n\n### 3D Object generation\n+ 3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction, [[paper]](http://arxiv.org/abs/1604.00449), [[github]](https://github.com/chrischoy/3D-R2N2)\n\n-----\n\n## GAN tutorials with easy and simple example code for starters\n+ [1D Generative Adversarial Network Demo](http://notebooks.aylien.com/research/gan/gan_simple.html)\n+ [starter from \"How to Train a GAN?\" at NIPS2016](https://github.com/soumith/ganhacks)\n+ [NIPS 2016 Tutorial: Generative Adversarial Networks](https://arxiv.org/abs/1701.00160)\n+ [OpenAI - Generative Models](https://blog.openai.com/generative-models/)\n+ [[paper]](), [[github]](), [[youtube]]()\n\n----\n\n## Implementations of various types of GANs collection\n+ [nashory/gans-collections.torch](https://github.com/nashory/gans-collection.torch), torch7\n+ [hwalsuklee/tensorflow-generative-model-collections](https://github.com/hwalsuklee/tensorflow-generative-model-collections), pytorch\n+ [wiseodd/generative-models](https://github.com/wiseodd/generative-models), both pytorch and tensorflow\n+ [aboev/arae-tf](https://github.com/aboev/arae-tf), tensorflow\n\n\n___\n\n## Trendy AI-application Articles\n+ [Artificial intelligence can say yes to the dress](https://qz.com/1090267/artificial-intelligence-can-now-show-you-how-those-pants-will-fit/)\n\n\n\n\n## Author\nMinchul Shin, [@nashory](https://github.com/nashory)  \n\n__Any recommendations to add to the list are welcome :)__  \n__Feel free to make pull requests!__\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnashory%2Fgans-awesome-applications","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnashory%2Fgans-awesome-applications","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnashory%2Fgans-awesome-applications/lists"}